mirror of
https://github.com/Ichinga-Samuel/aiomql.git
synced 2026-08-02 23:07:45 +00:00
testdata
This commit is contained in:
Ichinga Samuel
+1
-1
@@ -658,7 +658,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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+327
@@ -0,0 +1,327 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f1039720-9692-4605-adf9-d37651958e4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from pandas import DataFrame, Series"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 108,
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"id": "8d39819f-2cac-437f-b5fc-633ca7443f8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"rs = DataFrame({0: range(10, 101, 10), 1: range(10, 20), 2: range(20, 40, 2)})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"id": "eb69c292-79e3-4105-b10c-6f4f5c22074f",
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"metadata": {},
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"outputs": [],
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"source": [
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"rs.set_index(2, drop=False, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 110,
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"id": "d7976bb8-05cb-4924-a6e2-90ea8af85d9d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>20</th>\n",
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" <td>10</td>\n",
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" <td>10</td>\n",
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" <td>20</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>22</th>\n",
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" <td>20</td>\n",
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" <td>11</td>\n",
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" <td>22</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>24</th>\n",
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" <td>30</td>\n",
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" <td>12</td>\n",
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" <td>24</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>26</th>\n",
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" <td>40</td>\n",
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" <td>13</td>\n",
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" <td>26</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>28</th>\n",
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" <td>50</td>\n",
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" <td>14</td>\n",
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" <td>28</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>30</th>\n",
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" <td>60</td>\n",
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" <td>15</td>\n",
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" <td>30</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>32</th>\n",
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" <td>70</td>\n",
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" <td>16</td>\n",
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" <td>32</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>34</th>\n",
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" <td>80</td>\n",
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" <td>17</td>\n",
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" <td>34</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>36</th>\n",
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" <td>90</td>\n",
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" <td>18</td>\n",
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" <td>36</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>38</th>\n",
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" <td>100</td>\n",
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" <td>19</td>\n",
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" <td>38</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2\n",
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"2 \n",
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"20 10 10 20\n",
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"22 20 11 22\n",
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"24 30 12 24\n",
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"26 40 13 26\n",
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"28 50 14 28\n",
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"30 60 15 30\n",
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"32 70 16 32\n",
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"34 80 17 34\n",
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"36 90 18 36\n",
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"38 100 19 38"
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]
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},
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"execution_count": 110,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 112,
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"id": "3c456100-4931-4fbd-a63d-531eaf735e70",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"5"
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]
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},
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"execution_count": 112,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rs.index.get_loc(rs[rs.index <= 31].index[-1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"id": "1a5a99bb-3fc4-458d-95b8-4e031f3e04c1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index([20, 22, 24, 26, 28, 30, 32, 34, 36, 38], dtype='int64', name=2)"
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]
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},
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rs.index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 79,
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"id": "2d53ccef-72e1-4378-818f-b60682bbd8b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"g = rs[rs.index <= 31].iloc[-1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"id": "7318351e-e185-4695-ada5-43596b4844e6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"np.int64(20)"
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]
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},
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"execution_count": 87,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rs.index[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 114,
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"id": "55441116-8c00-4fd1-94c8-939320c6dfc4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[25, 26, 27, 28, 29]"
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]
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},
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"execution_count": 114,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"t = list(range(30))\n",
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"t[25:30]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 121,
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"id": "6ef28e4c-15a5-4fe5-aa66-10ba464f1256",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[6, 7, 8, 9]"
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]
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},
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"execution_count": 121,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"t[6: 10]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 126,
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"id": "f15cf18c-54cb-4624-8d18-3998bde2b9ba",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"None\n"
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]
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}
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],
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"source": [
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"p = 0 or None\n",
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"print(p)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "47dd4064-dde6-4824-a12b-ecf334bf4ebe",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@@ -58,6 +58,7 @@ class TestData:
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rates = self.rates[symbol][timeframe.name]
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start = int(datetime.timestamp(date_from)) if isinstance(date_from, datetime) else int(date_from)
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start = round_down(start, timeframe.time)
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start = rates[rates.index <= start].index
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start = rates.index.get_loc(start)
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end = start + count
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return rates.iloc[start:end].to_numpy()
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@@ -65,13 +66,15 @@ class TestData:
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def get_rates_from_pos(self, symbol: str, timeframe: TimeFrame, start_pos: int, count: int) -> np.ndarray:
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rates = self.rates[symbol][timeframe.name]
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end = -start_pos + count
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end = end or None
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return rates.iloc[-start_pos:end].to_numpy()
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def get_rates_range(self, symbol: str, timeframe: TimeFrame, date_from: datetime, date_to: datetime) -> np.ndarray:
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rates = self.rates[symbol][timeframe.name]
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start = int(datetime.timestamp(date_from))
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start = round_down(start, timeframe.time)
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start = round_down(int(datetime.timestamp(date_from)), timeframe.time)
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start = rates[rates.index <= start].iloc[-1].index
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end = round_up(int(datetime.timestamp(date_to)), timeframe.time)
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end = rates[rates.index >= end].index
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return rates.loc[start:end].to_numpy()
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def get_ticks_from(self, symbol: str, date_from: datetime | float, count: int, flags: CopyTicks) -> DataFrame:
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